1- // Code generated automatically by TMVA for Inference of Model file [Conv_d100_L1_B1.onnx] at [Mon Nov 8 14:01:59 2021]
1+ // Code generated automatically by TMVA for Inference of Model file [Conv_d100_L1_B1.onnx] at [Sat Nov 20 10:46:47 2021]
22#include < vector>
33#include " TMVA/SOFIE_common.hxx"
44#include < fstream>
@@ -12,12 +12,14 @@ namespace BLAS{
1212 const float * beta, float * C, const int * ldc);
1313}// BLAS
1414struct Session {
15- float tensor_conv0bias[ 2 ] = {} ;
16- float tensor_conv0weight[ 50 ] = {} ;
17- std::vector<float > fTensor_conv0biasbcast = std::vector<float >(20000 );
18- float * tensor_conv0biasbcast = fTensor_conv0biasbcast .data();
15+ std::vector< float > fTensor_conv0bias = std::vector< float >( 2 ) ;
16+ float * tensor_conv0bias = fTensor_conv0bias .data() ;
17+ std::vector<float > fTensor_conv0weight = std::vector<float >(50 );
18+ float * tensor_conv0weight = fTensor_conv0weight .data();
1919std::vector<float > fTensor_4 = std::vector<float >(20000 );
2020float * tensor_4 = fTensor_4 .data();
21+ std::vector<float > fTensor_conv0biasbcast = std::vector<float >(20000 );
22+ float * tensor_conv0biasbcast = fTensor_conv0biasbcast .data();
2123std::vector<float > fTensor_3 = std::vector<float >(20000 );
2224float * tensor_3 = fTensor_3 .data();
2325
@@ -37,23 +39,23 @@ Session(std::string filename ="") {
3739 int length;
3840 f >> tensor_name >> length;
3941 if (tensor_name != " tensor_conv0bias"
40- std::cout << " Error in tensor name : expected tensor name is tensor_conv0bias read " << tensor_name << std::endl ;
41- throw std::runtime_error (" tmva-sofie failed to read the correct tensor name "
42+ std::string err_msg = " TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_conv0bias , read " + tensor_name;
43+ throw std::runtime_error (err_msg );
4244 }
4345 if (length != 2 ) {
44- std::cout << " Error in tensor size : expected tensor size is 2 read " << length << std::endl ;
45- throw std::runtime_error (" tmva-sofie failed to read the correct tensor size "
46+ std::string err_msg = " TMVA-SOFIE failed to read the correct tensor size; expected size is 2 , read " + std::to_string (length) ;
47+ throw std::runtime_error (err_msg );
4648 }
4749 for (int i =0 ; i < length; ++i)
4850 f >> tensor_conv0bias[i];
4951 f >> tensor_name >> length;
5052 if (tensor_name != " tensor_conv0weight"
51- std::cout << " Error in tensor name : expected tensor name is tensor_conv0weight read " << tensor_name << std::endl ;
52- throw std::runtime_error (" tmva-sofie failed to read the correct tensor name "
53+ std::string err_msg = " TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_conv0weight , read " + tensor_name;
54+ throw std::runtime_error (err_msg );
5355 }
5456 if (length != 50 ) {
55- std::cout << " Error in tensor size : expected tensor size is 50 read " << length << std::endl ;
56- throw std::runtime_error (" tmva-sofie failed to read the correct tensor size "
57+ std::string err_msg = " TMVA-SOFIE failed to read the correct tensor size; expected size is 50 , read " + std::to_string (length) ;
58+ throw std::runtime_error (err_msg );
5759 }
5860 for (int i =0 ; i < length; ++i)
5961 f >> tensor_conv0weight[i];
@@ -63,71 +65,69 @@ Session(std::string filename ="") {
6365 std::vector<size_t > newShape = { 2 , 100 , 100 };
6466 oldShape.resize (newShape.size (), 1 .);
6567 float * newData_ptr = TMVA::Experimental::SOFIE::UTILITY::Unidirectional_broadcast<float >(tensor_conv0bias, oldShape, newShape);
66- int length = TMVA::Experimental::SOFIE::ConvertShapeToLength (newShape);
6768 for (int i = 0 ; i < 1 ; i++)
68- std::copy (newData_ptr, newData_ptr + length , tensor_conv0biasbcast + i * length);
69+ std::copy (newData_ptr, newData_ptr + 20000 , tensor_conv0biasbcast + i * 20000 );
70+ delete [] newData_ptr;
6971 }
7072}
7173
7274std::vector<float > infer (float * tensor_input){
7375
7476// ---- operator Conv op_0
75- float * op_0_f = fVec_op_0_f .data ();
76- for (std::size_t k = 0 ; k < 2 ; k++) {
77- for (std::size_t d = 0 ; d < 1 ; d++) {
78- for (std::size_t h = 0 ; h < 5 ; h++) {
79- for (std::size_t w = 0 ; w < 5 ; w++) {
80- op_0_f[k * 25 + d * 25 + h * 5 + w * 1 ] = tensor_conv0weight[k * 25 + d * 25 + h * 5 + w ];
81- }
82- }
83- }
84- }
85- float * op_0_xpad = fVec_op_0_xpad .data ();
86- for (size_t c = 0 ; c < 1 ; c++) {
87- for (size_t h = 0 ; h < 100 ; h++) {
88- size_t xpad_offset = c * 10816 + (h + 2 ) * 104 + 2 ;
89- size_t x_offset = c * 10000 + h * 100 ;
90- std::copy (tensor_input + x_offset, tensor_input + x_offset + 100 , op_0_xpad + xpad_offset);
91- }
92- }
93- char op_0_transA = ' T'
94- char op_0_transB = ' N'
95- int op_0_m = 10000 ;
96- int op_0_n = 2 ;
97- int op_0_k = 25 ;
98- float op_0_alpha = 1.0 ;
99- float op_0_beta = 0.0 ;
100- float * op_0_xcol = fVec_op_0_xcol .data ();
101- for (size_t n = 0 ; n < 1 ; n++) {
102- size_t op_0_index = 0 ;
103- size_t offseth = 0 ;
104- for (size_t h = 0 ; h < 100 ; h += 1 ) {
105- for (size_t w = 0 ; w < 100 ;w += 1 ) {
106- size_t offsetc = 0 ;
107- for (size_t c = 0 ; c < 1 ; c++) {
108- size_t offsetx = 0 ;
109- for (size_t x = 0 ; x < 5 ; x++) {
110- size_t offset = offsetc + offseth + offsetx + w;
111- std::copy (op_0_xpad + offset, op_0_xpad + offset + 5 , op_0_xcol + op_0_index);
112- op_0_index += 5 ;
113- offsetx += 104 ;
114- }
115- offsetc += 10816 ;
116- }
117- }
118- offseth += 104 ;
119- }
120- BLAS::sgemm_ (&op_0_transA, &op_0_transB, &op_0_m, &op_0_n, &op_0_k, &op_0_alpha, op_0_xcol, &op_0_k,
121- op_0_f, &op_0_k, &op_0_beta, tensor_3, &op_0_m);
122- }
123- int op_0_size = 20000 ;
124- float op_0_gamma = 1.0 ;
125- int op_0_incx = 1 ;
126- int op_0_incy = 1 ;
127- BLAS::saxpy_ (&op_0_size, &op_0_gamma, tensor_conv0biasbcast, &op_0_incx, tensor_3, &op_0_incy);
128- for (int id = 0 ; id < 20000 ; id++){
129- tensor_4[id] = ((tensor_3[id] > 0 )? tensor_3[id] : 0 );
130- }
77+ float * op_0_f = fVec_op_0_f .data ();
78+ for (std::size_t k = 0 ; k < 2 ; k++) {
79+ for (std::size_t d = 0 ; d < 1 ; d++) {
80+ for (std::size_t h = 0 ; h < 5 ; h++) {
81+ for (std::size_t w = 0 ; w < 5 ; w++) {
82+ op_0_f[k * 25 + d * 25 + h * 5 + w * 1 ] = tensor_conv0weight[k * 25 + d * 25 + h * 5 + w ];
83+ }
84+ }
85+ }
86+ }
87+ char op_0_transA = ' T'
88+ char op_0_transB = ' N'
89+ int op_0_m = 10000 ;
90+ int op_0_n = 2 ;
91+ int op_0_k = 25 ;
92+ float op_0_alpha = 1.0 ;
93+ float op_0_beta = 0.0 ;
94+ float * op_0_xpad = fVec_op_0_xpad .data ();
95+ float * op_0_xcol = fVec_op_0_xcol .data ();
96+ size_t offset_tensor_3 = 0 ;
97+ for (size_t n = 0 ; n < 1 ; n++) {
98+ for (size_t c = 0 ; c < 1 ; c++) {
99+ for (size_t h = 0 ; h < 100 ; h++) {
100+ size_t xpad_offset = c * 10816 + (h + 2 ) * 104 + 2 ;
101+ size_t x_offset = c * 10000 + h * 100 ;
102+ std::copy (tensor_input + x_offset, tensor_input + x_offset + 100 , op_0_xpad + xpad_offset);
103+ }
104+ }
105+ size_t op_0_index = 0 ;
106+ for (size_t h = 0 ; h < 100 ; h += 1 ) {
107+ for (size_t w = 0 ; w < 100 ;w += 1 ) {
108+ for (size_t c = 0 ; c < 1 ; c++) {
109+ for (size_t x = 0 ; x < 5 ; x++) {
110+ size_t offset = c * 10816 + (h + x) * 104 + w;
111+ std::copy (op_0_xpad + offset, op_0_xpad + offset + 5 , op_0_xcol + op_0_index);
112+ op_0_index += 5 ;
113+ }
114+ }
115+ }
116+ }
117+ BLAS::sgemm_ (&op_0_transA, &op_0_transB, &op_0_m, &op_0_n, &op_0_k, &op_0_alpha, op_0_xcol, &op_0_k,
118+ op_0_f, &op_0_k, &op_0_beta, tensor_3 + offset_tensor_3, &op_0_m);
119+ offset_tensor_3 += 20000 ;
120+ }
121+ int op_0_size = 20000 ;
122+ float op_0_gamma = 1.0 ;
123+ int op_0_incx = 1 ;
124+ int op_0_incy = 1 ;
125+ BLAS::saxpy_ (&op_0_size, &op_0_gamma, tensor_conv0biasbcast, &op_0_incx, tensor_3, &op_0_incy);
126+
127+ // ------ RELU
128+ for (int id = 0 ; id < 20000 ; id++){
129+ tensor_4[id] = ((tensor_3[id] > 0 )? tensor_3[id] : 0 );
130+ }
131131 std::vector<float > ret (tensor_4, tensor_4 + 20000 );
132132 return ret;
133133}
0 commit comments